Intelligent transportation systems are considered as the advanced applications that provide novel services relating to traffic management and a convenient use of transport networks. Among a plurality of others, the issue of ensuring driving safety through driving behavior analysis is an important requirement that has received wide attention. As it is clear that the driving safety bears a direct co-relation with the driver's normal and abnormal driving behaviors, many systems for detecting driver's driving behavior have been developed and are widely used. These systems usually include detection of a driver's physiological signal(s), such as the movement of the driver's head, the changes in the driver's heartbeat, the moving track of the steering wheel, the driver's eye movement and the like.
The driving behavior analysis systems may be essentially used for the applications like customized auto-insurance. For such applications, an insurance premium for a driver is based on the driver's normal and abnormal driving behaviors. This behavior is known usage based insurance (UBI) which is a telematics application. As widely understood telematics is a technology relating to the collection of vehicle data as well as driving data and transmitting the data over the telecom network to a central server for further analysis. The purpose of data analysis is to identify a given driving behavior in terms of potential risk induced. The analysis may lead to quantitative figures: for example, driving scores that are used for underwriting. On a different context, such telematics applications may be used to identify safe driving behavior on a daily basis. The application may identify behavior (on a particular trip) with respect to a baseline behavior where the baseline behavior is classified based upon a priori knowledge; often captured as part of blind profiling. Here, traffic psychology forms a particular basis of classifying a driver in terms of potential for driving error.
Conventionally, the driving pattern classification requires the collection of a plurality of sensor data. The minimum set of sensors that may be required for the analysis and classification may include, but are not limited to, accelerometer sensors, gyroscope sensors, vehicle speed capturing sensors, location capturing sensors, weather capturing sensors, etc. Further, to test the risk level due to driving behavior, people have analyzed statistically the historical record of the number of car crashes. Additionally, researchers have computed a threshold value for the aggregated and/or processed data. The threshold value defines a “crash” and the measured values are compared with it to assess the probability of a crash.
The conventional system needs to collect data from a plurality of sensors embedded in a moving vehicle, wherein the sensors may include, but are not limited to, accelerometers, speed, GPS, time, etc. There exists a need to define specifically anomalous behavior like hard stop, harsh acceleration, hard bump, etc. For such analysis, assigning of a threshold value below which the behavior is treated as normal, computing of the total number of such specific violations and calculating of a relative risk is a time consuming process.
In the light of foregoing problems, there exists a need for a system and a method that can provide a one stop solution for driving behavior analysis in order to generate a driving behavior of a driver that can effectively overcome the deficiencies and technical limitations described hereinabove. Further, there also exists a need for a system and a method that can efficiently compute a driving score with minimum requirement of sensors to sense multiple parameters of a moving vehicle.